Intraoperative Ultrasound in Endoscopic Sagittal Suture Synostosis to Optimize Incision Planning and Avoid Misdiagnosis
Bibliographic record
Abstract
Endoscopy-assisted craniectomy with lateral osteotomies and postoperative helmet molding therapy is a widely used approach in managing sagittal suture craniosynostosis. Generally, the incisions are placed just posterior to the anterior fontanel and just anterior to the posterior fontanel and lambdoid sutures, and accurate incision placement optimizes the safe separation of the superior sagittal sinus. The authors present their 10 year experience with an ultrasound-assisted approach to identify the lambdoid sutures and precisely place the skin incisions. The authors included all patients in care at their institution between 2010 and 2023 who operated for sagittal suture craniosynostosis with endoscopy-assisted craniectomy with lateral osteotomies and postoperative helmet molding therapy. A retrospective review of clinical parameters, surgical data, as well as outcomes, and imaging studies was performed. One hundred patients were operated during the observation period. The mean age was 3.9 ± 3.5 (range: 2.7-6.4) months. Intraoperative ultrasound was documented in 61% of cases (n = 61). In 100% of cases, the incisions were placed behind the anterior and in front of the posterior fontanel, as planned with ultrasound. In 2 additional cases, intraoperative sonography identified a patent sagittal suture in the operating room. A histopathological review showed suture ossification in 100% of operated cases with available reports. Using this technique of ultrasound-guided identification of the lambdoid suture/posterior fontanel, as well as coronal suture/anterior fontanel, may aid in the adequate placement of skin incisions. Patent sutures can be identified in clinically misdiagnosed patients. This study reaffirms the overall utility of ultrasound in pediatric operative neurosurgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".